Enhanced UAV Path Planning Using the Tangent Intersection Guidance (TIG) Algorithm

Hichem Cheriet; Badra Khellat Kihel; Samira Chouraqui

doi:10.14313/jamris-2026-018

.blurhash-client-img { display: none !important; }

Enhanced UAV Path Planning Using the Tangent Intersection Guidance (TIG) Algorithm

Journal of Automation, Mobile Robotics and Intelligent Systems

Volume 20 (2026): Issue 2 (June 2026)

By: Hichem Cheriet, Badra Khellat Kihel and Samira Chouraqui

Open Access

|Jun 2026

Figures & Tables

Tangent lines from start point S to elliptic obstacle

3 Potential scenarios resulting in infeasible paths

Waypoint generation using virtual ellipse technique

Generated path using dynamic path planner in a partially-known environment

D-TIG planner algorithm steps in an unknown environment

Example of a generated path without smoothing

A smoothed path using a quadratic Bézier curve with collision

Generated paths in static environments on a short map (C1)

Generated paths in static environments on a large map (C5)

Generated paths in static environments on a sparse map (C9)

Generated paths in static environments on a dense map (C13)

Generated paths in unknown environment on a short map (C17)

Generated paths in unknown environment on a large map (C21)

Generated paths in unknown environment on a sparse map (C25)

Generated paths in unknown environment on a dense map (C29)

Generated paths in a partially known environment with pop-up obstacles on a short map (C19)

Generated paths in a partially known environment with pop-up obstacles on a long map (C20)

Generated paths in a partially known environment with pop-up obstacles on a sparse map (C25)

Generated paths using D-TIG in a partially known environment with pop-up obstacles on dense maps

Comparison of different dynamic path planning algorithms across different scenarios

Map Type	Case	Path Length			Time			Turning Radius
Map Type	Case	D-TIG	APF	APPATT	D-TIG	APF	APPATT	D-TIG	APF	APPATT
Short	C17	520.46	844.00	519.67	0.03	0.11	0.04	1.72	35.01	2.22
	C18	485.87	718.00	491.07	0.03	0.08	0.03	0.64	147.98	3.45
	C19	509.74	531.00	510.58	0.01	0.07	0.01	0.82	63.21	1.57
	C20	625.68	885.00	N/A	0.04	0.12	N/A	3.08	13.03	N/A
Large	C21	1010.41	1406.00	1010.01	0.07	0.14	0.05	1.38	10.65	1.90
	C22	1125.06	1419.00	1124.14	0.06	0.14	0.04	1.99	9.66	2.28
	C23	1036.91	1401.00	1033.27	0.04	0.14	0.03	3.52	1200.42	2.86
	C24	990.64	N/A	994.53	0.03	N/A	0.04	1.95	N/A	3.40
Sparse	C25	495.73	687.00	496.27	0.02	0.06	0.01	1.08	7.26	1.65
	C26	516.49	532.00	518.80	0.01	0.04	0.01	1.08	2.98	2.17
	C27	489.01	552.00	489.39	0.01	0.05	0.01	0.63	184.25	1.18
	C28	508.54	536.00	513.59	0.01	0.05	0.01	2.21	8.04	3.59
Dense	C29	558.54	N/A	N/A	0.05	N/A	N/A	6.51	N/A	N/A
	C30	562.17	N/A	N/A	0.06	N/A	N/A	5.92	N/A	N/A
	C31	505.38	N/A	505.14	0.04	N/A	0.05	1.86	N/A	2.59
	C32	564.15	N/A	N/A	0.06	N/A	N/A	7.55	N/A	N/A

Comparison of Different Dynamic Path Planning Algorithms Across Different Scenarios

Map Type	Case	Path Length		Time		Turning Radius
Map Type	Case	D-TIG	APPATT	D-TIG	APPATT	D-TIG	APPATT
Short	C17	520.46	571.56	0.02	0.03	0.55	4.12
	C18	496.99	502.81	0.01	0.01	1.62	1.36
	C19	513.46	520.09	0.005	0.007	0.97	1.47
Large	C21	1017.12	N/A	0.02	N/A	2.03	N/A
	C22	1130.68	1244.94	0.08	0.01	1.56	4.44
	C23	1027.61	1044.94	0.03	0.01	2.70	3.99
Sparse	C25	494.90	522.74	0.001	0.003	0.97	4.27
	C26	534.93	N/A	0.002	N/A	2.2	N/A
	C27	495.09	561.69	0.008	0.002	1.48	4.42

Comparison of different static path planning algorithms across four scenarios

Map Type	Case	Path Length						Time						Turning Radius
Map Type	Case	S-TIG	A*	PRM	RRT	APPATT	TG	S-TIG	A*	PRM	RRT	APPATT	TG	S-TIG	A*	PRM	RRT	APPATT	TG
Short	C1	493.84	510.06	949.33	566.49	495.29	493.59	0.01	8.44	3.65	0.18	0.09	0.20	0.53	32.20	58.35	14.96	1.45	0.53
	C2	511.89	533.16	661.20	595.68	N/A	511.60	0.07	11.72	3.65	0.27	N/A	2.73	0.68	32.20	42.18	16.84	N/A	0.46
	C3	520.53	545.25	587.87	931.31	531.38	514.25	0.06	9.26	3.68	0.64	0.21	2.81	2.81	49.48	30.59	36.17	1.99	1.86
	C4	527.59	557.87	645.85	840.31	584.34	528.50	0.06	10.38	3.36	0.14	0.06	2.02	2.34	46.33	37.94	26.21	2.72	2.34
Large	C5	1063.13	1156.40	1071.65	1429.14	1135.71	1063.76	0.11	73.00	5.25	0.77	0.15	73.48	1.97	55.76	6.43	34.66	9.28	1.72
	C6	1004.27	1022.01	1040.68	1313.74	1064.00	1004.14	0.02	92.65	5.09	0.35	0.10	0.14	0.16	10.21	7.20	34.58	1.87	0.14
	C7	1241.35	1289.83	1253.50	1558.46	N/A	1238.23	0.05	80.09	5.88	0.78	N/A	4.13	1.49	69.90	6.69	44.56	N/A	1.07
	C8	1001.01	1054.11	1019.10	1254.34	1925.46	999.66	0.09	72.85	5.25	0.39	0.14	8.57	1.67	52.62	5.08	35.32	10.29	1.42
Sparse	C9	522.01	545.98	524.81	706.33	543.33	522.24	0.04	16.00	9.85	0.11	0.01	0.83	1.02	18.06	2.30	8.14	5.55	1.02
	C10	515.36	548.42	526.48	714.74	515.30	515.38	0.03	17.47	9.02	0.02	0.03	0.04	0.25	14.92	4.76	12.66	0.24	0.24
	C11	514.74	524.83	525.51	643.16	524.88	514.81	0.01	12.82	9.44	0.11	0.05	0.03	0.37	13.35	6.32	16.09	0.69	0.37
	C12	511.61	520.28	525.26	613.53	N/A	509.00	0.06	16.69	9.36	0.09	N/A	0.16	2.05	16.49	6.08	15.28	N/A	1.14
Dense	C13	601.13	652.94	613.57	713.55	N/A	602.23	0.32	6.71	10.90	0.34	N/A	57.50	2.97	69.90	9.38	17.87	N/A	2.96
	C14	509.77	562.94	515.49	604.97	N/A	509.69	0.29	6.73	9.25	0.47	N/A	5.26	2.46	62.05	6.15	12.70	N/A	2.14
	C15	686.06	698.61	638.75	868.91	N/A	686.05	0.68	6.16	11.60	0.42	N/A	62.59	18.90	68.33	13.63	23.39	N/A	18.90
	C16	572.76	644.14	574.83	711.90	N/A	568.13	0.32	4.35	10.10	0.34	N/A	61.27	11.80	52.62	7.33	17.47	N/A	3.80

Definitions of main notations

Notations	Description
S	Start-point
T	Target-point
w	A waypoint generated by the tangent planner
CurrentSet	A set to store candidate waypoints
ClosedSet	A set to store visited waypoints
treatedSet	A set that records tangent points that have already been calculated

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.14313/jamris-2026-018 | Journal eISSN: 2080-2145 | Journal ISSN: 1897-8649

Journal RSS Feed

Language: English

Page range: 30 - 52

Submitted on: Apr 6, 2025

Accepted on: Apr 23, 2025

Published on: Jun 24, 2026

Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Path planning,

UAV navigation,

Elliptic tangent graph,

obstacle avoidance,

unmanned aerial vehicle

Related subjects:

Computer sciences,

Artificial intelligence,

Engineering,

Electrical engineering,

Control engineering, metrology and testing,

Mechanical engineering,

Fundamentals of mechanical engineering

© 2026 Hichem Cheriet, Badra Khellat Kihel, Samira Chouraqui, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 20 (2026): Issue 2 (June 2026)

Enhanced UAV Path Planning Using the Tangent Intersection Guidance (TIG) Algorithm

Figures & Tables

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Figure 12.

Figure 13.

Figure 14.

Figure 15.

Figure 16.

Figure 17.

Figure 18.

Figure 19.

Figure 20.

Figure 21.

Figure 22.

Comparison of different dynamic path planning algorithms across different scenarios

Comparison of Different Dynamic Path Planning Algorithms Across Different Scenarios

Comparison of different static path planning algorithms across four scenarios

Definitions of main notations

Paradigm

My account